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首页> 外文会议>Annual international ACM SIGIR conference on research and development in information retrieval >Collaborative Filtering with Privacy via Factor Analysis
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Collaborative Filtering with Privacy via Factor Analysis

机译：通过因子分析与隐私的协同过滤

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Collaborative filtering (CF) is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today's systems have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we describe a new method for collaborative filtering which protects the privacy of individual data. The method is based on a probabilistic factor analysis model. Privacy protection is provided by a peer-to-peer protocol which is described elsewhere, but outlined in this paper. The factor analysis approach handles missing data without requiring default values for them. We give several experiments that suggest that this is most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. Finally, we suggest applications of the approach to other kinds of statistical analyses of survey or questionaire data.
机译：协作过滤（CF）在电子商务中是有价值的，以及用于音乐，电影，新闻等的直接建议，但今天的系统有几个缺点，包括隐私风险。随着我们走向普遍存在的计算，个人有一个潜在的潜力，以分享有关场所和景点的各种信息，看到和购买，但隐私风险严重。在本文中，我们描述了一种用于协作过滤的新方法，这些方法可以保护各个数据的隐私。该方法基于概率因子分析模型。隐私保护由其他地方描述的对等协议提供，但本文概述。因子分析方法处理缺失的数据而不需要它们的默认值。我们给出了几个实验，表明这是迄今为止最准确的方法。新算法在先前的算法上具有速度和存储的其他优点。最后，我们建议对调查或问卷数据的其他统计分析的方法进行应用。

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